Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study
Xiao Chen,
Benyi Cao and
Somayeh Pouramini
Energy, 2023, vol. 270, issue C
Abstract:
A large amount of energy consumed globally is done by buildings, also, buildings are responsible for a great portion of greenhouse gas emissions. With progress in smart sensors and devices, a new generation of smarter and more context-aware building controllers can be developed. Consequently, zone-level surrogate artificial neural networks are used herein, where indoor temperature, occupancy, and weather data are the inputs. A new metaheuristic optimization algorithm, called Chaotic Satin Bowerbird Optimization Algorithm (CSBOA) is employed for the minimization of energy consumption. 24-hour schedules of the heating setpoint of each zone are created for an office building located in Edinburgh, Scotland. Two modes of optimization including day-ahead and model predictive control are applied for each hour. The consumption of energy decreased by 26% during a test week in Feb in comparison to the base case approach of heating. By definition of a time-of-use tariff, the cost of energy consumption is decreased by around 28%.
Keywords: Energy consumption reduction; Cost reduction; Chaotic satin Bowerbird optimization algorithm; Artificial neural network; Model predictive control; Setpoint schedule of heating; HVAC control (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (8)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:270:y:2023:i:c:s0360544223002682
DOI: 10.1016/j.energy.2023.126874
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